Energy Storage Configuration and Scheduling for Rail Transit Based on a Beetle Antennae-Optimized Back Propagation Neural Network Prediction Algorithm

Lidong Han; Lei  Yang; Lang  Su

doi:10.15282/ijame.22.4.2025.10.0987

Authors

Lidong Han School of Mechanical & Electrical Engineering, Suzhou Global Institute, Suzhou, 215163, China
Lei Yang School of Mechanical & Electrical Engineering, Suzhou Global Institute, Suzhou, 215163, China
Lang Su School of Mechanical & Electrical Engineering, Suzhou Global Institute, Suzhou, 215163, China

DOI:

https://doi.org/10.15282/ijame.22.4.2025.10.0987

Keywords:

Beetle antennae optimization algorithm, BP neural network, Rail transit, Energy storage configuration, Scheduling optimization

Abstract

To meet the growing energy management needs of the rail transit system, this study explores the configuration and scheduling strategies of rail transit energy storage. An innovative prediction-allocation-dispatch co-optimization framework based on the deep fusion of the Beetle Antennae search algorithm and back-propagation neural network is proposed. This framework solves the problem of the co-optimization of renewable energy output uncertainty and the economy of energy storage. The study adopts a Beetle Antennae-optimized backpropagation neural network prediction algorithm to improve the prediction accuracy of wind and solar power generation. A hybrid energy storage configuration system is designed by comprehensively considering the energy storage life and cost, and a multi-time scale energy scheduling optimization scheme is proposed. The experiment showed that the optimization prediction algorithm could significantly improve prediction accuracy and had superior iterative optimization effects. Compared with traditional prediction algorithms, this optimization algorithm reduced the mean square error of photovoltaic and wind power prediction by 41.11% and 34.02% respectively and reduced the time consumption by 29.95% and 33.21% compared to other algorithms. Meanwhile, the hybrid model had significant advantages in energy storage costs and net benefits, with a total scheduling cost reduction of 17.95% compared to other energy storage systems and a response speed improvement of 13.98%. This indicates that the proposed optimization algorithm and configuration model can enhance the energy utilization function and scheduling flexibility of the rail transit system, supporting the sustainable development of rail transit.

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